Theses and Dissertations
ORCID
https://orcid.org/0009-0002-9376-8897
Issuing Body
Mississippi State University
Advisor
Ball, John E.
Committee Member
Gurbuz, Ali Cafer
Committee Member
Iqbal, Umar
Date of Degree
12-8-2023
Document Type
Graduate Thesis - Open Access
Major
Electrical and Computer Engineering
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing (SPaT) messages to vehicles, providing information on current traffic light phases and time until the next phase change which enables the vehicles to adjust their speed and behavior in real-time. The simulation demonstrates accurate traffic light detection, with vehicles receiving SPaT messages, showcasing the system’s effectiveness in a multi-intersection scenario.
Recommended Citation
Rahman, Mahfuzur, "Traffic light detection and V2I communications of an autonomous vehicle with the traffic light for an effective intersection navigation using MAVS simulation" (2023). Theses and Dissertations. 6058.
https://scholarsjunction.msstate.edu/td/6058
Included in
Navigation, Guidance, Control, and Dynamics Commons, Signal Processing Commons, Systems and Communications Commons